Faculty & Research

Brij Disa Centre for Data Science and Artificial Intelligence

About the Centre

The Brij Disa Centre for Data Science and Artificial Intelligence (CDSA) at the Indian Institute of Management Ahmedabad (IIMA) provides a common platform to faculty, scholars, and practitioners for conducting and disseminating cutting-edge research on data analytics and artificial intelligence that offers solutions applicable to business, governance, and policy.
Besides generating action oriented insights, CDSA is also responsible for dissemination of the knowledge generated to a wider audience both within and outside the realm of the Institute. Seminars, workshops, and conferences are regular activities at the Centre, which are conducted to reach out to and engage with stakeholders.
The Centre aims to forge synergistic and collaborative relationships between scholars and practitioners in dataintensive organizations, besides undertaking case-based research to understand the current industry practice and develop case studies for classroom teaching.
Furthermore, through its collaboration with the industry, CDSA takes up challenging consulting projects of considerable practical importance. These projects are targeted at providing an opportunity for students to participate in projects that aim at outcomes that can further benefit the organisation and the business, at large.
A key offering from the Centre is the Annual Report, which would provide a holistic view of the Data Science and Artificial Intelligence industry, identify challenges and gaps, gauge scope of the industry and offer plausible solutions that can be utilised by the industry and policy makers.

About the Centre

The Brij Disa Centre for Data Science and Artificial Intelligence (CDSA) at the Indian Institute of Management Ahmedabad (IIMA) provides a common platform to faculty, scholars, and practitioners for conducting and disseminating cutting-edge research on data analytics and artificial intelligence that offers solutions applicable to business, governance, and policy.

Besides generating action oriented insights, CDSA is also responsible for dissemination of the knowledge generated to a wider audience both within and outside the realm of the Institute. Seminars, workshops, and conferences are regular activities at the Centre, which are conducted to reach out to and engage with stakeholders.

The Centre aims to forge synergistic and collaborative relationships between scholars and practitioners in dataintensive organizations, besides undertaking case-based research to understand the current industry practice and develop case studies for classroom teaching.

Furthermore, through its collaboration with the industry, CDSA takes up challenging consulting projects of considerable practical importance. These projects are targeted at providing an opportunity for students to participate in projects that aim at outcomes that can further benefit the organisation and the business, at large.

A key offering from the Centre is the Annual Report, which would provide a holistic view of the Data Science and Artificial Intelligence industry, identify challenges and gaps, gauge scope of the industry and offer plausible solutions that can be utilised by the industry and policy makers.

Centre Activities

Common platform for faculty, scholars, and industry

Build partnerships to undertake collaborative research and jointly organise workshops

Enable policymakers with reports on trends and progression of analytic tools, techniques and other resources

Share knowledge through seminars, fireside chats, workshops, etc. on research and applications of topical interest

Facilitate businesses by connecting them with researchers to solve challenging business problems

Archives

Reports

AI in India - A Strategic Necessity

The Centre forges synergistic and collaborative relationships between scholars and practitioners in data intensive organizations, besides undertaking case-based research to understand the current industry practice and develop case studies for classroom teaching.

Events

LSO 2023

The Brij Disa Centre for Data Science and Artificial Intelligence, in collaboration with the Indian Institute of Technology Kanpur, successfully organised the third LSO Summer School and Conference in April 2023. This event provided a platform for researchers and practitioners to exchange ideas on optimization, fostering collaboration and showcasing recent developments in theory and applications in the field.

Centre's Research Dissemination

Projects

Supporting Parents to Impact Foundational Literacy and Numeracy (FLN) of Children

Prof. Ambrish Dongre and Dr. Neaketa Chawla

India has made rapid strides in ensuring access to primary schooling. Data from a variety of sources also suggests that the percentage of children in the age-group of 6 to 10 years enrolled in school is near universal, and the drop-out rates low. However, concerns about learning outcomes persist. It is well accepted that a large fraction of enrolled children are way below the level expected at the grade in which they are enrolled. 

Saajha, a non-governmental organization, is attempting to tackle this challenge by focusing on the parents of children attending government primary schools. This approach, of reaching out to parents and involving them in child’s education, has been receiving increased attention recently with many organizations and governments undertaking such efforts.

Saajha first conducts community drives through which they on-board parents on their Whatsapp platform. After this they conduct Hindi and Math assessments of children over the telephone. Depending upon the learning level of the child, educational content related to Foundational Literacy and Numeracy (FLN) skills is shared over WhatsApp with respective parents. Parents are expected to show this content to their children. Saajha subsequently conducts periodic assessments to check whether the learning level is improving. 

These assessments are conducted by 'Saajhedars,' parents who have previously used Saajha's WhatsApp platform. Parents can also call the `Saajha’ helpline number for admission-related inquiries and other information regarding government schools in Delhi.

Through this project, we are collaborating with Saajha to design and implement multiple experiments to improve their operational efficiencies.

‘Scandalous’ and ‘Obscene’ Trademark Law: Determining the scope of morality-based proscriptions in Indian Law

Prof. M P Ram Mohan

Morality-based restrictions on trademarks are prevalent in trademark legislations worldwide, existing in 163 out of 164 WTO member states. In 2019, the United States Supreme Court held that such restrictions fall afoul of their free speech jurisprudence. Yet, in the process, the Court explicitly emphasized the significance of linguistic regulation rooted in moral principles within trademark law. Despite having housed these provisions for over four decades, no legislative or judicial body has interpreted morality-based proscriptions in Indian law. The administrative practices of the Indian Trade Marks Registrar and a review of the Indian Trade Marks Register demonstrate extreme inconsistency and incoherence in applying the ban against ‘scandalous’ and ‘obscene’ content in Indian trade mark law. These findings highlight the urgent need for comprehensive guidelines that combine legislative heritage and insights from Australian law to establish a consistent framework for identifying the import and meaning of ‘scandal’ and ‘obscenity’ in Indian law.

Optimal Merkle Trees for Blockchain Transactions

Prof: Sachin Jayaswal

A Merkle tree, also called a hash tree, is a well-known tool to efficiently validate a data element in a set without revealing the entire set. It thus forms a critical component of blockchain technology as it enables secure validation of blockchain transactions. It stores the balance of each blockchain account as a hash value on its leaf nodes, which must be updated after each transaction. The efficiency of hash updates on a Merkle tree depends on the relative distribution of the accounts on the tree: as two accounts that frequently transact between them move closer to the root, fewer nodes need to be read and updated. In this work, we aim to develop an integer programming-based mathematical model for constructing a Merkle tree that can be efficiently updated. Further, we aim to develop algorithms to solve the resulting model efficiently.

Hiring for the Future – A People Analytics Approach

Prof. Aditya Christopher Moses

The future of work is a critical aspect for many organizations. A 2020 report by the World Economic Forum suggests that among the various challenges faced by organization one of the most critical areas is skill gaps. They argue that skill gaps continue to remain high as in-demand skills across jobs change in the short term. The top skills and skill groups which employers see as rising in prominence in the lead up to 2025 include groups such as critical thinking and analysis as well as problem-solving, and skills in self-management such as active learning, resilience, stress tolerance and flexibility. On average, companies estimate that around 40% of workers will require reskilling of six months or less and 94% of business leaders report that they expect employees to pick up new skills on the job, a sharp uptake from 65% in 2018.

 

The changing nature of work and the exponential technology development imply that employees need to constantly re-skill and up-skill. In the current environment, while knowledge can be accessed via multiple sources the behaviours to develop oneself become more important. What behaviours will organizations require for ensuring they have a workforce that can reskill and upskill exponentially? This will be the primary area of research for this study.

 

Using a data-driven approach, this study uses surveys and NLP to understand which behavioural traits enable re-skilling at pace. We will employ text-mining methods and techniques to identify behavioural traits that help in re-skilling. The insights from this will be further validated and tested using a survey instrument administered to a large sample of individuals

Financial networks from big data: A multivariate time series based approach

Prof. Anindya Chakrabarti

Financial markets exhibit non-trivial comovement and dependency structure. The standard approach in the finance literature is to consider the market in its aggregate form. A more recent 'data'-oriented approach emphasizes a more granular decomposition of the market so that the aggregate dynamics can be broken down into contributions arising from individual assets. This leads to two analytical problems. First, one has to necessarily deal with a large amount of data such that the process scales with the volume of data (large N and large T where T>>N). Two, analyzing such a large volume of data requires toolkits which are at the intersection of econometrics and machine learning. In this project, the goal is to construct large scale financial networks based on multivariate time series data to capture the dynamics of the system. The main idea is to provide an algorithmic approach to convert time series into networks such that the properties of time series are also inherited by the resulting network. The spectral structure of the comovement network is known to capture, at least partially, the booms and busts in the markets. Here, we take up two specific problems. One, how reliably does the spectral structure reflect the system for the case where T~N. Two, a large chunk of the literature on networks construction depends on bivariate modelling which is subject to failure due to multiple hypothesis testing. Therefore, an imminent question is how to construct a network with a direct multivariate model.

High-frequency trading: Measuring latency from big data

Prof. Anirban Banerjee

Over the last decade, the Indian market has seen significant growth in algorithmic trading and more specifically, high-frequency trading (HFT) activity. During this period, we have witnessed a significant change in the trading landscape as presently close to half of the trading volume in the stock exchanges is contributed by algorithms. This rise has not always been smooth as there have been calls for regulations to restrict algorithmic trading activity due to the fear of probable market manipulation.

 

Latency is considered one of the most important market parameters for HFTs. Using a large novel dataset of order and trade level data from the NSE, we would like to inspect how the latency in the Indian market has changed and if that has caused any shift in the way HFTs operate. We would also like to observe how the different market quality parameters have evolved over this time.

An iterative gradient-based bilevel approach for hyperparameter tuning in machine learning

Prof. Ankur Sinha 

Hyperparameter tuning in the area of machine learning is often achieved using naive techniques, such as random search and grid search that only lead to an approximate set of hyperparameters. Although techniques such as Bayesian optimization perform an intelligent search on the domain of hyperparameters, it does not guarantee an optimal solution. A major drawback of most of these approaches is that as the number of hyperparameters increases, the search domain increases exponentially, thereby increasing the computational cost and making the approaches slow. The hyperparameter optimization problem is inherently a bilevel optimization task, and there exist studies that have attempted bilevel solution methodologies for solving this problem. These techniques often assume a unique set of weights that minimizes the loss on the training set. Such an assumption is violated by deep learning architectures. Our study is on gradient-based bilevel optimization method for solving the hyperparameter optimization problem. The method is general and can be easily applied to any class of machine learning algorithms that involve continuous hyperparameters.

Voice AI Effectiveness for Debt Collection

Prof. Anuj Kapoor

Conversational AI Chatbots are rapidly evolving as new technologies with both business potential and customer reactance. This study exploits large-scale field experiment data on thousands of customers who are randomized to receive highly structured outbound sales calls from chatbots. We vary features like the gender of the bot along with the formal or informal tone of the bot. In this paper, we propose a dynamic outbound call experimentation policy. The proposed approach extends multi-armed bandit (MAB) algorithms, from statistical machine learning, to include microeconomic choice theory. Our automated outbound call policy solves this MAB problem using a scalable distribution-free algorithm. Beyond the actual experiment, we plan to counterfactual simulations to evaluate a range of alternative model specifications and allocation rules in MAB policies.

Can an AI Coach Help You Lose More Weight Than a Human Coach: Empirical Evidence From a Mobile Fitness Tracking App

Prof. Anuj Kapoor

Artificial intelligence(AI) assisted tools are increasingly being used in health care contexts to provide advice and motivation. But whether AI can be a good or even better substitute for human involvement in these contexts is an open question. We provide empirical evidence to answer this question specifically in the context of fitness tracking mobile applications (apps). In addition to facilitating the tracking of activity and food intake, such apps provide advice and motivation in the form of targeted messages to their consumers, and this can be done through human coaches or an AI coach. An AI coach allows these apps to scale their offerings to a larger number of consumers, available on demand to consumers, and potentially more finely targeted by leveraging vast amounts of data. On the other hand, human coaches might be better placed to show empathy, and consumers might also feel more accountable to humans. We compare human and AI coaches on their effectiveness in helping consumers achieve their weight-loss goals. Our empirical analysis is in the context of a large-scale mobile app that offers consumers different levels of subscription plans with human and AI coaches respectively, and specifically compares adopters of the two kinds of plans on their weight loss and goal achievement. We address the potential self-selection in plans by employing a matching-based approach. We find, for our sample of almost 65000 consumers that human-based plans do better than those in AI-based plans in helping them achieve their goals, but that this differs by consumer characteristics including age, gender and body mass index (BMI).

Purchase/Biding behaviour of new and used anthropomorphized and non-anthropomorphized toaster products on eBay and classifying the toasters using ML techniques

Prof. Hyokjin Kwak

Major empirical methods: web scrapping, data pre-processing, independent t-test, machine learning classification method (CNN and ResNet-32).

This project aims to study how anthropomorphized ‘brand new’ and ‘used’ toaster products affect consumer purchase or bidding behaviour. To do this, I scrapped all the toaster data information like product name, number of consumers watching the product, bidding details etc., from eBay website, toaster products were then manually labelled as Anthropomorphized "AB" or Non- Anthropomorphized "Non-AB." Exploratory data analysis (EDA) was used to look at the attributes of the data. IBM SPSS software is also used to analyse the Independent sample t-test. This test compares the means of two independent groups, AB and Non-AB, to see if there is statistical evidence that the relevant attribute means are significantly different.

Also, multiple deep learning approaches were used to classify the AB and NAB toaster images.

Effective Amul Brand Positioning Through Topical Ads and Brand Mascot: A Sentimental Analysis on Twitter Data

Prof. Hyokjin Kwak

Major empirical methods: web scrapping, data analysis, sentimental analysis.

The objective of this study is to check the purchasing behaviour of consumers in response to creative advertisements that have been posted on Amul's twitter handles and to also apply sentimental classification techniques to the comments that have been posted on Amul's twitter handles. Initially, I compiled all available data on Amul's most successful advertisements from 2019 to 2021, which can be found on the company's website and neatly labeled according to year. Information about advertisements was then culled from the Amul Twitter account and matched with captions taken directly from the company's website. The properties of the data were investigated using exploratory data analysis (EDA) methods. IBM SPSS is also used to analyse the results of the independent sample t-test, which compares the means of two groups to determine whether or not there is statistical evidence of the relevant attributes are significantly different. In addition to that, a sentimental analysis was carried out on the user comments left on the advertisement that was uploaded to Amul's Twitter handle

(https://twitter.com/Amul_Coop)

Sentimental Analysis on Amazon Book Reviews in India (vs US

Prof. Hyokjin Kwak

Major empirical methods: web scrapping, data pre-processing, sentimental and emotions analysis.

This research will compare the emotional and sentimental analysis of book reviews that were posted on Amazon.in (India) and Amazon.com (US) by taking into consideration of other characteristics such as book ratings, book cost, discounts available on the book, etc. This study will focus mostly on the sentiments and emotions expressed in Amazon USA and India book reviews. I explored various algorithms like VADER (Valence Aware Dictionary and Sentiment Reasoner), Textblob, SentiBERT etc. to detect the sentiments present in the product reviews also explored emotions like happy, fear, disgust, anticipation, joy, sadness, surprise, and trust.

Production Recommendation using Product Reviews from Amazon India (vs US)

Prof. Hyokjin Kwak

Major empirical methods: web scrapping, data pre-processing, text analysis which includes identifying bi-gram and trigram words

In this study, I will be performing an analysis of the textual contents, beginning with the reviews of the product and continuing all the way through to the purchase recommendation. In addition, a semi-automatic method will be utilised to extract terms from the text in reviews, and a knowledge graph will be utilised. The extracted phrases were connected to the various technical aspects of the items. After that, vector representations of the graph elements will be trained, which will result in a significant improvement in the overall quality of the recommendations. I intend to look into Adaptive Text Rank, which is based on a set of technical characteristics and a collection of sentiment words; the SOTA BERT model, which matches terms with the technical features of the products; the TransE method, which trains vector representations of graph elements; and the ABAE method, which highlights important characteristics for products based on a collection of reviews.

Building Fake Review Detection Model for Amazon India (vs US)

Prof. Hyokjin Kwak

Major empirical methods: web scrapping, data pre-processing, text analysis which includes identifying bi-gram and trigram words, collection of large labelled dataset of fake and non- fake reviews from official sources.

The most recent AI text generation models, such as GPT-2 and GPT-3, along with a variety of transformer models, can be utilised to generate fake computer-generated reviews. Researchers in the field of data science have demonstrated how artificial intelligence reviews may be generated and identified with the use of machine learning. Additionally, fake reviews can be purchased in bulk from various sources. Researchers have uncovered a wide range of potential traits that can help humans and models determine the difference between a fake review and a genuine one. These features were discovered after studying fake reviews in great detail. Because fake reviews frequently utilise the same language, the review content itself is generally considered to be the most important aspect. This is especially true in cases where multiple reviews were written by the same individual, firm, or other sources. Review length, Sentiment, Helpfulness, Reviews per user, and Verified review are some of the often-used features that can be noticed in studies on fake review detection. In order to create a model that is able to differentiate between the fake reviews that are posted on Amazon.com and Amazon.in, I will be conducting research into text-classification algorithms and NLP pre-processing techniques, such as count vectorization and TF-IDF, as well as machine and deep learning techniques

Topic Modelling on Online Product Reviews from Amazon India (vs USA)

Prof. Hyokjin Kwak

Major empirical methods: web scrapping, data pre-processing, text analysis, Latent Dirichlet Allocation (LDA)

The technique of automatically identifying topics that are present in a text item and deriving hidden patterns that are represented by a text corpus is referred to as topic modelling. The usage of topic models is beneficial for a variety of tasks, including the clustering of texts, the organisation of huge blocks of textual data, the recovery of information from unstructured text, and the selection of features.

My goal here is to extract a set number of relevant word groups from the reviews based on sentiment, i.e., positive, negative, or neutral. These word groups are essentially the issues that will aid in determining what the customers are actually talking about in the reviews. This will inform us which subjects are frequently addressed by Indian and American reviews when they favour or dislike the product. This can be expanded upon in terms of emotions as well.

Data-driven auction design: A computational approach

Prof. Jeevant Rampal

Auctions are often used to sell property rights for liquor licenses, spectrum licenses, land and mineral rights, and construction projects etc. This project investigates potential improvements in these auctions using a computational data-driven approach. The first part of this project will be to collect primary data of the participants and their choices in auctions. Subsequently, using the game-theoretic properties of the chosen auction design, we will computationally estimate the true (unobservable) value distribution across players of the object(s) being auctioned (e.g., liquor licenses). The estimation method used will be non-parametric “distance minimization” between the observed out-of-sample distribution of bids, and the predicted out-of-sample distribution of bids using optimally calibrated parameter values. E.g., Athey, Levin, and Seira (QJE 2011) use their estimated model to make comparative static predictions and test that for fit against data from timber auctions.

 

Finally, to analyse which auction design would have best met the various aims of the auction designer, we will use the calibrated model, parameters, and the estimated valuations of the bidders. In particular, using these we will simulate the revenue, efficiency, and other metrics of importance for different auction designs. In addition to the use of simulation described above, to analyse alternate auction designs, we will use simulations of variations of the estimated model, parameters (like risk aversion, budgets etc.), and value distributions to analyse the different rates with which different auction designs can meet the various possible aims of the auction designer.

Multi-period Facility Interdiction Problem

Prof. Sachin Jayaswal 

We propose to study a multiperiod interdiction problem, in which the leader (attacker) with a limited interdiction budget decides the sequence of facilities to interdict (destroy) over time so as to inflict the maximum cumulative damage to the follower. The follower's objective is to serve a given set of demand points from the surviving subset of facilities the minimum cumulative cost across all periods. for this, his decisions include the assignments of demand nodes to the surviving facilities and the allocation of his limited budget to the revival of interdicted facilities and the protection of the surviving facilities against their interdiction in the future periods. The multi-period version of the problem, which is the focus of the proposed study, presents additional complexity due to the leader's interdiction decisions constrained by the follower's protection decisions. The objective of the proposed study is to design efficient exact solution methods for this challenging bilevel integer program..

Causes, Symptoms and Consequences of Sociocultural polarization

Prof. Samrat Gupta 

The Information and Communication Technology (ICT) provides users unparalleled access to information from around the globe. In spite of demographic differences, people can communicate, express and evolve their opinions on topics ranging from politics to culture. The wide-ranging information exchange on digital media can lead to two scenarios viz. formation of public sphere or formation of echo chambers. While the public sphere, which promotes greater diversity, is a well-researched domain, substantially less research has been conducted on echo chambers in relation to socio-cultural events. Despite the huge affirmative impact of socio-cultural events on society, the proliferation of controversies around them and reinforcement through echo chambers is collectively having malefic effects on societies. Recent controversies around socio-cultural products such as movies, painting, books, cartoons, etc. resulted in serious outcomes. For example, Indian movie Padmavat brought polarization of public perception which further reinforced through echo chambers and escalated into widespread agitations. It led to mass destruction of property and human suffering during agitation. We believe this represents a mounting problem for society, one that is likely to intensify in the era of social media. Thus, understanding the causes, symptoms and consequences of socio-cultural polarization is critical and would be valuable for developing interventions to reduce unhealthy societal and organizational polarisations.

Models of implied volatility and information content of option prices

Prof. Sobhesh kumar Agarwalla and Prof Vineet Virmani

The proposed research project on modeling implied volatility (IV) and understanding the information content of option prices is part of our larger research agenda on studying ways to quantify uncertainty in financial markets, focusing on India. Traders in options markets do not usually quote option prices, but the volatility implied by them. IV is that volatility input to the famous Black-Scholes option pricing formula such that the Black-Scholes prices match the market price of the options. It has been observed that IV is not a constant but varies systematically with strike/delta and expiration date. The shape of the observed relationship between implied volatility and strike is called volatility smile or skew. In this project, we plan to explore various ways of modeling the dynamics of volatility smile using variants of state-space models and the Kalman Filter.The proposed research project on modeling implied volatility (IV) and understanding the information content of option prices is part of our larger research agenda on studying ways to quantify uncertainty in financial markets, focusing on India. Traders in options markets do not usually quote option prices, but the volatility implied by them. IV is that volatility input to the famous Black-Scholes option pricing formula such that the Black-Scholes prices match the market price of the options. It has been observed that IV is not a constant but varies systematically with strike/delta and expiration date. The shape of the observed relationship between implied volatility and strike is called volatility smile or skew. In this project, we plan to explore various ways of modeling the dynamics of volatility smile using variants of state-space models and the Kalman Filter.

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Advisory Board

Anand Deshpande

Persistent Systems

Sanjiv Das

Santa Clara University

Sunil Gupta

Harvard Business School

Tiziana Di Matteo

Kings College London

Members

Name  University
Sobhesh Kumar Agarwalla IIM Ahmedabad
Cheong Siew Ann Nanyang Technological University
Anirban Banerjee IIM Ahmedabad
Arindam Banerjee IIM Ahmedabad
Dhiman Bhadra IIM Ahmedabad
Indranil Bose IIM Ahmedabad
Anindya S. Chakrabarti IIM Ahmedabad
Swanand Deodhar IIM Ahmedabad
Anil Deolalikar UC Riverside
Samrat Gupta IIM Ahmedabad
Sachin Jayaswal IIM Ahmedabad
Anuj Kapoor IIM Ahmedabad
Hyokjin Kwak IIM Ahmedabad
Andrea Lodi École Polytechnique de Montréal
Thomas Lux Kiel University
Tanmoy Majilla IIM Ahmedabad
Adrija Majumdar IIM Ahmedabad
Pekka Malo Aalto University School of Business
Sheri Markose University of Essex
Mohsen Mohaghegh IIM Ahmedabad
M P Ram Mohan IIM Ahmedabad
Aditya Christopher Moses IIM Ahmedabad
Soumya Mukhopadhyay IIM Ahmedabad
Sundaravalli Narayanaswami IIM Ahmedabad
Viswanath Pingali IIM Ahmedabad
Sudha Ram University of Arizona
Jeevant Rampal IIM Ahmedabad
Neelkant Rawal Wells Fargo
Sriram Sankaranarayanan IIM Ahmedabad
Suprateek Sarker University of Virginia
Pankaj Setia IIM Ahmedabad
Avinash Sharma IIIT Hyderabad
Peng Shi Wisconsin School of Business
Hemant Kumar Singh University of New South Wales
Pranav Singh IIM Ahmedabad
Ankur Sinha IIM Ahmedabad
Sitabhra Sinha The Institute of Mathematical Science, Chennai
Chetan Soman IIM Ahmedabad
Karthik Sriram IIM Ahmedabad
Anish Sugathan IIM Ahmedabad
Abhishek Tripathi Perfios
Arvind Tripathi University of Auckland Business School
Ellapulli Vasudevan IIM Ahmedabad
Sanjay Verma IIM Ahmedabad

Members

Name University
Abhishek Tripathi Perfios
Aditya Christopher Moses IIM Ahmedabad
Adrija Majumdar IIM Ahmedabad
Ambrish Dongre IIM Ahmedabad
Andrea Lodi École Polytechnique de Montréal
Ankur Sinha IIM Ahmedabad
Anil Deolalikar UC Riverside
Anindya S. Chakrabarti IIM Ahmedabad
Anirban Banerjee IIM Ahmedabad
Anish Sugathan     IIM Ahmedabad
Anuj Kapoor IIM Ahmedabad
Arindam Banerjee IIM Ahmedabad
Arvind Tripathi University of Auckland Business School
Avinash Sharma IIIT Hyderabad
Cheong Siew Ann     Nanyang Technological University
Chetan Soman IIM Ahmedabad
Dhiman Bhadra     IIM Ahmedabad
Ellapulli Vasudevan IIM Ahmedabad
Hemant Kumar Singh University of New South Wales
Hyokjin Kwak     IIM Ahmedabad
Indranil IIM Ahmedabad
Jeevant Rampal IIM Ahmedabad
Karthik Sriram IIM Ahmedabad
Mohsen Mohaghegh IIM Ahmedabad
M P Ram Mohan IIM Ahmedabad
Neelkant Rawal Wells Fargo
Pankaj Setia IIM Ahmedabad
Peng Shi Wisconsin School of Business
Pekka Malo Aalto University School of Business
Pranav Singh IIM Ahmedabad
Pritha Dev IIM Ahmedabad
Sachin Jayaswal IIM Ahmedabad
Samrat Gupta     IIM Ahmedabda
Sanjay Verma IIM Ahmedabda
Sheri Markose University of Essex
Sitabhra Sinha The Institute of Mathematical Science, Chennai
Sobhesh Kumar Agarwalla IIM Ahmedabad
Soumya Mukhopadhyay     IIM Ahmedabad
Sriram Sankaranarayanan     IIM Ahmedabad
Sudha Ram University of Arizona
Sundaravalli Narayanaswami IIM Ahmedabad
Suprateek Sarker University of Virginia
Swanand Deodhar     IIM Ahmedabad
Tanmoy Majilla         IIM Ahmedabad
Thomas Lux Kiel University
Viswanath Pingali IIM Ahmedabad

 

Team

Name  Designation
Present Members
Debjit Ghatak Centre Head
Neaketa Chawla Post-doctoral Research Associate
Nebu Varghese Pre-doctoral Research Associate
Rahul Sharma Pre-doctoral Research Associate
Sayantan Pramanick Pre-doctoral Research Associate
Saswot Nayak Pre-doctoral Research Associate
Ashish Talvekar Pre-doctoral Research Associate
Harsh Parikh Pre-doctoral Research Associate
Chaitanya Agarwal Pre-doctoral Research Associate
Lucky Khanna Pre-doctoral Research Associate
Vijay V Venkitesh Pre-doctoral Research Associate
Dibyajyoti Chakrabarti Pre-doctoral Research Associate
Amita Todkar Pre-doctoral Research Associate
Rohal Harchandani Pre-doctoral Research Associate
Dhaval Pujara Pre-doctoral Research Associate
Tanya Gauni Pre-doctoral Research Associate
GVK Sai Sarath Intern
Priyanshu Kumar Intern
Shubhankar Sahu Intern
Aritra Banerjee Intern
Anjali Nair Centre Secretary

 

Past Members

Kushal Bhalla Post-doctoral Research Associate
Arnab Chakrabarti Post-doctoral Research Associate
Vani Dwivedi Pandya Pre-doctoral Research Associate
Prince Roy Pre-doctoral Research Associate
Shivam Kumar Pre-doctoral Research Associate
Viswash Mehta Pre-doctoral Research Associate
Kulvinder Kaur Post-doctoral Research Associate
Satender Pre-doctoral Research Associate
Vaishnav Garg Pre-doctoral Research Associate
Sayantan Pramanick Pre Doctoral Research Associate
Tanya Garg Pre Doctoral Research Associate

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